Real time vehicle speed prediction using gas-kinetic traffic modeling
Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, macroscopic and kinetic traffic modeling approaches are investigated. We present a speed prediction algorithm, KTM-SP, based on gas-kinetic traffic modeling. Experimental results show that the proposed algorithm gave good prediction results on real traffic data.